N. Srivastava, P. Kuila, N. Chatterjee, A. K. Subramani, Jan Akbar
{"title":"基于遗传算法的n组分可修系统维修策略选择","authors":"N. Srivastava, P. Kuila, N. Chatterjee, A. K. Subramani, Jan Akbar","doi":"10.5937/sjm17-28807","DOIUrl":null,"url":null,"abstract":"A typical manufacturing system consists of a large number of repairable components/ machines which age with time and require maintenance. This paper proposes a novel maintenance policy selection method using genetic algorithm. Where, maintenance problem is formulated for n-component repairable system to minimize the total maintenance cost. The various maintenance policies and repairable components are represented in the form of chromosomes, initially various chromosomes are randomly generated which are then assessed and selected using fitness value and then crossover and mutation function is performed to obtain a better chromosome. Several iterations are performed till the desired results is achieved. The proposed algorithm is further explained and validated through an illustrative example.","PeriodicalId":44603,"journal":{"name":"Serbian Journal of Management","volume":"1 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Maintenance policy selection of n-component repairable system using Genetic Algorithm\",\"authors\":\"N. Srivastava, P. Kuila, N. Chatterjee, A. K. Subramani, Jan Akbar\",\"doi\":\"10.5937/sjm17-28807\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A typical manufacturing system consists of a large number of repairable components/ machines which age with time and require maintenance. This paper proposes a novel maintenance policy selection method using genetic algorithm. Where, maintenance problem is formulated for n-component repairable system to minimize the total maintenance cost. The various maintenance policies and repairable components are represented in the form of chromosomes, initially various chromosomes are randomly generated which are then assessed and selected using fitness value and then crossover and mutation function is performed to obtain a better chromosome. Several iterations are performed till the desired results is achieved. The proposed algorithm is further explained and validated through an illustrative example.\",\"PeriodicalId\":44603,\"journal\":{\"name\":\"Serbian Journal of Management\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Serbian Journal of Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5937/sjm17-28807\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Serbian Journal of Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5937/sjm17-28807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MANAGEMENT","Score":null,"Total":0}
Maintenance policy selection of n-component repairable system using Genetic Algorithm
A typical manufacturing system consists of a large number of repairable components/ machines which age with time and require maintenance. This paper proposes a novel maintenance policy selection method using genetic algorithm. Where, maintenance problem is formulated for n-component repairable system to minimize the total maintenance cost. The various maintenance policies and repairable components are represented in the form of chromosomes, initially various chromosomes are randomly generated which are then assessed and selected using fitness value and then crossover and mutation function is performed to obtain a better chromosome. Several iterations are performed till the desired results is achieved. The proposed algorithm is further explained and validated through an illustrative example.
期刊介绍:
Technical Faculty in Bor, University of Belgrade has started publishing the journal called Serbian Journal of Management during the year 2006. This journal is an international medium for the publication of work on the theory and practice of management science.